Abstract
Searchable symmetric encryption (SSE) schemes are commonly proposed to enable search in a protected unstructured documents such as email archives or any set of sensitive text files. However, some SSE schemes have been recently proposed in order to protect relational databases. Most of the previous attacks on SSE schemes have only targeted its common use case, protecting unstructured data. In this work, we propose a new inference attack on relational databases protected via SSE schemes. Our inference attack enables a passive adversary with only basic knowledge about the meta-data information of the target relational database to recover the attribute names of some observed queries. This violates query privacy since the attribute name of a query is secret.
M. A. Abdelraheem—Most of this work was done while the author was a postdoc at RISE SICS in Sweden.
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- 1.
OpenEMR is a well known open source medical software supporting Electronic Medical Records (EMR).
- 2.
If the cardinality |a| is not unique, then k subsets will exist where k is the number of attributes whose cardinalities are equal to |a|.
- 3.
There is one-to-one correspondence between education and education-num.
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Acknowledgments
This work was supported by European Union’s Horizon 2020 research and innovation programme under grant agreement No 644814, the PaaSword project within the ICT Programme ICT-07-2014: Advanced Cloud Infrastructures and Services.
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A Example Explaining Our Attribute-Name Recovery Attack
A Example Explaining Our Attribute-Name Recovery Attack
In the following, we give a toy example to demonstrate our attack. Assume that we have a relational database table as shown in Table 4. Using a deterministic encryption algorithm to encrypt the “Sex” and“Education” columns and using an order preserving encryption will transform our relational database table to an encrypted relational database table as shown in Table 5. However, most secure SSE schemes will transform an inverted index such as the one displayed in Table 6 into a length-hiding encrypted index where the server does not know the frequency or result length of each keyword token before being queried.
After observing all the queries issued on the encrypted index shown in Table 6. Our attribute-name recovery attack tries to resolve the attribute name of each observed query by exploiting the access pattern leakage. Figure 1 shows three graphs whose nodes represent the observed queries. The graph on the left shows the server’s knowledge (represented by the frequencies or result lengths of observed queries gained from the access pattern leakage) before launching our attacks. When we apply the Attribute-Name recovery attack using only as background knowledge the meta-data information about the table and the number of records, the Server will know only the attribute names, “Education”, “Sex” and “Age” represented by the graph on the middle in Fig. 1. Note that Naveed et al. [22] attack recovers column names and values of the encrypted database table shown in Table 5 using public background data. However, our attribute-name recovery attack recovers the query issued on the encrypted index shown in Table 7 using only meta-data information about the database table in addition to the number of records which can be leaked by some SSE schemes or guessed by the attacker.
Moreover, when we apply both the Attribute-Name recovery attack and the Relational-Count attack using the frequency distribution knowledge, the Server will know both the attribute names and their corresponding actual values. This additional knowledge is represented in Fig. 1 by the graph on the right.
Nodes on the graphs represent all the possible queries that can be issued in the relational database index table shown in Table 6 after being encrypted by an SSE scheme. An edge between nodes (queries) exists if the intersection between their corresponding result sets is non-zero. The graph on the left shows queries as nodes of a graph labeled by their result lengths before applying our attacks. The graph on the middle shows what the server will learn after applying our attribute-name recovery attack. Note that the green color refers to the “Sex” attribute name and the blue color refers to the “Education” attribute name and the red color refers to the “Age” attribute name. The graph on the right shows what the server could learn after applying our second attack (Attribute-Name recovery attack combined with the Relational-Count attack).
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Abdelraheem, M.A., Andersson, T., Gehrmann, C., Glackin, C. (2018). Practical Attacks on Relational Databases Protected via Searchable Encryption. In: Chen, L., Manulis, M., Schneider, S. (eds) Information Security. ISC 2018. Lecture Notes in Computer Science(), vol 11060. Springer, Cham. https://doi.org/10.1007/978-3-319-99136-8_10
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